Today, most processors in computer systems provide a 64-bit datapath architecture. The 64-bit datapath allows operations such as read, write, add, subtract, and multiply on the entire 64 bits of data at a time. This added bandwidth has significantly improved performance of the processors.
However, the data types of many real world applications do not utilize the full 64 bits in data processing. For example, in digital signal processing (DSP) applications involving audio, video, and graphics data processing, the light and sound values are usually represented by data types of 8, 12, 16, or 24 bit numbers. This is because people typically are not able to distinguish the levels of light and sound beyond the levels represented by these numbers of bits. Hence, DSP applications typically require data types far less than the full 64 bits provided in the datapath in most computer systems.
In initial applications, the entire datapath was used to compute an image or sound values. For example, an 8 or 16 bit number representing a pixel or sound value was loaded into a 64-bit number. Then, an arithmetic operation, such as an add or multiply, was performed on the entire 64-bit number. This method proved inefficient, however, as it was soon realized that not all the data bits were being utilized in the process since digital representation of a sound or pixel requires far fewer bits. Thus, in order to utilize the entire datapath, a multitude of smaller numbers were packed into the 64 bit doubleword.
Furthermore, much of data processing in DSP applications involve repetitive and parallel processing of small integer data types using loops. To take advantage of this repetitive and parallel data process, a number of today's processors implements single instruction multiple data (SIMD) in the instruction architecture. For instance, the Intel Pentium MMX™ chips incorporate a set of SIMD instructions to boost multimedia performance.
Prior Art FIG. 1 illustrates an exemplary single instruction multiple data instruction process. Exemplary registers, vs and vt, in a processor are of 64-bit width. Each register is packed with four 16-bit data elements fetched from memory: register vs contains vs[0], vs[1], vs[2], and vs[3] and register vt contains vt[0], vt[1], vt[2], and vt[3]. The registers in essence contain a vector of N elements. To add elements of matching index, an add instruction adds, independently, each of the element pairs of matching index from vs and vt. A third register, vd, of 64-bit width may be used to store the result. For example, vs[0] is added to vt[0] and its result is stored into vd[0]. Similarly, vd[1], vd[2], and vd[3] store the sum of vs and vd elements of corresponding indexes. Hence, a single add operation on the 64-bit vector results in 4 simultaneous additions on each of the 16-bit elements. On the other hand, if 8-bit elements were packed into the registers, one add operation performs 8 independent additions in parallel. Consequently, when a SIMD arithmetic instruction, such as addition, subtraction, or multiply, is performed on the data in the 64-bit datapath, the operation actually performs multiple numbers of operations independently and in parallel on each of the smaller elements comprising the 64 bit datapath.
Unfortunately, however, an arithmetic operation such as add and multiply on SIMD vectors typically increases the number of significant bits in the result. For instance, an addition of two n-bit numbers may result in a number of n+1 bits. Moreover, a multiplication of two n-bit numbers produces a number of 2n bit width. Hence, the results of an arithmetic operation on a SIMD vector may not be accurate to a desired significant bit.
Furthermore, the nature of multimedia DSP applications often increases inaccuracies in significant bits. For example, many DSP algorithms implemented in DSP applications require a series of computations producing partial results that are larger or bigger, in terms of significant number of bits, than the final result. Since the final result does not fully account for the significant bits of these partial results, the final result may not accurately reflect the ideal result, which takes into account all significant bits of the intermediate results.
To recapture the full significant bits in a SIMD vector arithmetic operation, the size of the data in bits for each individual element was typically boosted or promoted to twice the size of the original data in bits. Thus, for multiplication on 8-bit elements in a SIMD vector for instance, the 8-bit elements were converted (i.e., unpacked) into 16-bit elements containing 8 significant bits to provide enough space to hold the subsequent product.
Unfortunately, however, the boost in the number of data bits largely undermined the benefits of SIMD vector scheme by reducing the speed of an arithmetic operation in half. This is because the boosting of data bits to twice the original size results in half as many data elements in a register. Hence, an operation on the entire 64-bit datapath comprised of 16-bit elements accomplishes only 4 operations in comparison to 8 operations on a 64-bit datapath comprised of 8-bit elements. In short, boosting a data size by X-fold results in performance reduction of (1/X)*100 percent. As a result, instead of an effective 64-bit datapath, the effective datapath was only 32-bits wide.
Thus, what is needed is a method and system for providing extended precision in SIMD vector arithmetic operations without sacrificing speed and performance.